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Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling

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ARCHIVES OF TOXICOLOGY 2021年第5期95卷 1683-1701页

作者： Li, Size Yu, Yiqun Bian, Xiaolan Yao, Li Li, Min Lou, Yan-Ru Yuan, Jing Lin, Hai-shu Liu, Lucy Han, Bing Xiang, Xiaoqiang Fudan Univ Sch Pharm Dept Clin Pharm & Pharm Adm Shanghai 201203 Peoples R China Fudan Univ Minhang Hosp Dept Pharm Shanghai 201199 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Dept Pharm Sch Med Shanghai 20025 Peoples R China Shenzhen Technol Univ Coll Pharm Shenzhen 518118 Guangdong Peoples R China Shanghai Qiangshi Informat Technol Co Ltd Shanghai Peoples R China

The risk of drug-induced liver injury (DILI) poses a major challenge for development of natural products derived from traditional Chinese medicines (NP-TCMs). It is urgent to find a new method for the safety assessment of the NP-TCMs. Recent study has reported an in vitro/in silico method to estimate the acceptable daily intake of hepatotoxic compounds using support vector machine (SVM) classifier and physiologically based pharmacokinetic (PBPK) modeling. However, this method is not suitable for estimating the dosing schedule of compounds which are administered in multiple daily doses. Thus, in this study, the method mentioned above was in particular optimized, and used to estimate the hepatotoxic plasma concentrations of 17 NP-TCMs. Additionally, the oral dosing schedules of the triptolide, emodin, matrine and oxymatrine were also predicted by the SVM classifier and PBPK modeling. The optimization included that: (1) in vitro cytotoxicity data of 28 training set compounds was optimized using benchmark concentrations (BMC) modeling;(2) AUC of the training set compound was used as the in vivo metric instead of C-max to better reflect the total daily exposure of compounds which are administered in multiple daily doses;(3) using the mean AUC in plasma as in vivo metric and BMC value as in vitro metric could achieve the better toxicity separation index (0.962 vs. 0.938);(4) The TSI for C-max and BMC values was 0.985 calculated in this study, and the results indicated that BMC modeling improved the separation performance. This optimized in vitro-in vivo extrapolation (IVIVE) workflow could extrapolate in vitro BMC to blood concentrations and the oral dosing schedule which are corresponding to certain risk of hepatotoxicity. The estimated safe dosing schedule of oxymatrine by this optimized method was close to the clinical recommended dosing regimen. The results indicate that the optimized method could be used to predict the dosing schedule of compounds administered in mul

关键词： support vector machine classifier Drug-induced liver injury Natural products derived from traditional Chinese medicines PBPK modeling In vitro to in vivo extrapolation

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machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study

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BRAIN AND BEHAVIOR 2023年第9期13卷 e3139页

作者： Eyvazpour, Reza Navi, Farhad Farkhondeh Tale Shakeri, Elmira Nikzad, Behzad Heysieattalab, Soomaayeh Iran Univ Sci & Technol IUST Sch Elect Engn Dept Biomed Engn Tehran Iran Univ Tabriz Dept Cognit Neurosci Tabriz Iran Allameh Tabatabai Univ Fac Management & Accounting Dept Business Management Tehran Iran Univ Tabriz Res Ctr Biosci & Biotechnol Neurobioscince Div Tabriz Iran

BackgroundDecision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt. MethodsThis study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. ResultsIntersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. ConclusionsThe findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

关键词： decision-making machine learning manager resting-state EEG support vector machine classifier

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Between therapy effect and false-positive result in animal experimentation

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BIOMEDICINE & PHARMACOTHERAPY 2023年第1期160卷 114317-114317页

作者： Sosnowski, Pawel Sass, Piotr Stanislawska-Sachadyn, Anna Krzeminski, Michal Sachadyn, Pawel Gdansk Univ Technol Lab Regenerat Biotechnol ul Narutowicza 11-12 PL-80233 Gdansk Poland Gdansk Univ Technol Dept Mol Biotechnol & Microbiol ul Narutowicza 11-12 PL-80233 Gdansk Poland Gdansk Univ Technol Inst Appl Math Fac Appl Phys & Math ul Narutowicza 11-12 PL-80233 Gdansk Poland

Despite the animal models' complexity, researchers tend to reduce the number of animals in experiments for expenses and ethical concerns. This tendency makes the risk of false-positive results, as statistical significance, the primary criterion to validate findings, often fails if testing small samples. This study aims to highlight such risks using an example from experimental regenerative therapy and propose a machine-learning solution to validate treatment effects. The example analysed was the pharmacological treatment of ear pinna punch wound healing in mice. Wound closure data analysed included eight groups treated with an epigenetic inhibitor, zebularine, and eight control groups receiving vehicle alone, of six mice each. We confirmed the zebularine healing effect for all 64 pairwise comparisons between treatment and control groups but also determined minor yet statistically significant differences between control groups in five of 28 possible comparisons. The occur-rences of significant differences between the control groups, regardless of standardised experimental conditions, indicate a risk of statistically significant effects in the case a compound lacking the desired biological activity is tested. Since the criterion of statistical significance itself can be confusing, we demonstrate a machine-learning algorithm trained on datasets representing treatment and control experiments as a helpful tool for validating treatment outcomes. We tested two machine-learning approaches, Naive Bayes and support vector machine classifiers. In contrast to the Mann-Whitney U-test, indicating enhanced healing effects for some control groups receiving saline alone, both machine-learning algorithms faultlessly assigned all animal groups receiving saline to the controls.

关键词： False -positive results Sample size Statistical significance Animal experimentation pharmacoregeneration machine -learning Na?ve Bayes classifier support vector machine classifier

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COVID-19 Fake News Prediction On Social Media Data

COVID-19 Fake News Prediction On Social Media Data

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IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future

作者： Ul Hussna, Asma Trisha, Iffat Immami Karim, Md Sanaul Alam, Md Golam Rabiul BRAC Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665400268

It is, to tell the truth, that the COVID-19 pandemic has put the whole world in a tough time, and sensitive information concerning COVID-19 has grown tremendously online. Most importantly, the gradual spread of fake news and misleading information during these hard times can have dire consequences, causing widespread panic and exacerbating the apparent threat of a pandemic that we cannot ignore. Because of the time-consuming nature of evidence gathering and careful truth-checking, people get confused between fallacious and trustworthy statement. So, we need a way to keep track of misinformation on social media. Most people think that all social media information is real information though, at the same time, it is a shame that some people misuse this social media platform for their own benefit by spreading misinformation. Many individuals take advantage by playing with the weaknesses of others. As a result, people around the world not only are facing COVID-19, they are also facing infodemics. To get rid of this kind of fake news, we have proposed a research model that can predict fake news related to the COVID-19 issue on social media data using classical classification methods such as multinomial naive bayes classifier, logistic regression classifier, and support vector machine classifier. Moreover, we have applied a deep learning based algorithm named distil BERT to accurately predict fake COVID-19 news. These approaches have been used in this paper to compare which technique is much more convenient for accurately predicting fake news about COVID-19 on social media posts. In addition, we have used a data-set that included 6424 social media posts.

关键词： COVID-19 distilBERT TF-IDF Multinomial Naive Bayes classifier Logistics Regression classifier support vector machine classifier pandemic infodemic

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Cuff-less Blood Pressure measurement from Wireless ECG and PPG signals 7

Cuff-less Blood Pressure measurement from Wireless ECG and P...

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7th IEEE International Symposium on Smart Electronic Systems (IEEE-iSES)

作者： Dave, Tejal Pandya, Utpal Joshi, Maulin Sarvajanik Coll Engn & Technol Dept Instrumentat & Control Surat India Sarvajanik Coll Engn & Technol Dept Elect & Commun Surat India

ISBN: (纸本)9781728187532

Continuous monitoring of blood pressure (BP) can control hypertension and cardiac diseases. Blood pressure measurement using cuff based technique provides intermittent measurement and inconvenient for long term monitoring. This work is focused on estimation of continuous blood pressure from electrocardiogram (ECG) and photoplethysmogram (PPG). The proposed work extracts ECG and PPG time domain features acquired through wireless hardware system. Using support vector Regression of machine learning, a light weight model for Blood Pressure estimation is trained. The proposed work is tested on wireless signals captured from 87 subjects using hardware device. According to the British Hypertension Society (BHS) standard, the proposed method achieves grade A in the estimation of systolic and diastolic pressure for wireless data. The values of mean error and standard deviation by proposed method are within limits of Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed work is helpful in wireless monitoring of patients to track the physiological conditions without interrupting their routine activities.

关键词： cuff-less blood pressure monitoring electrocardiogram (ECG) photoplehysmogram (PPG) support vector machine classifier support vector regression (SVR)

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Burn characterization using object-oriented hyperspectral image classification

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JOURNAL OF BIOPHOTONICS 2022年第11期15卷 e202200106页

作者： Parasca, Sorin Viorel Calin, Mihaela Antonina Carol Davila Univ Med & Pharm Bucharest Bucharest Romania Emergency Clin Hosp Plast Reconstruct Surg & Burn Bucharest Romania Natl Inst Res & Dev Optoelect INOE 2000 409 Atomistilor StPOB MG5 Magurele 077125 Ilfov Romania

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% +/- 0.60%), precision (97.30% +/- 2.46%), sensitivity (97.23% +/- 3.02%) and specificity (98.02% +/- 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

关键词： burn depth precision segmentation sensitivity specificity support vector machine classifier

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A Novel Cough Detection Algorithm for COVID-19 Surveillance at Public Places 8

A Novel Cough Detection Algorithm for COVID-19 Surveillance ...

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8th International Conference on Smart Computing and Communications (ICSCC)

作者： Sreedharan, Deepak Raj, Subodh M. S. George, Sudhish N. Ashok, S. Natl Inst Technol Calicut Dept Elect Engn Kozhikode India Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode India

ISBN: (纸本)9781728196879

A worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals at public spaces is a hectic task. Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on common symptoms exhibited. In this paper, a new algorithm is developed for detecting repetitive coughing action which is the main symptom in COVID-19 cases, and thus detecting people with COVID-19 based on it. The performance of the proposed system is tested on an existing sneeze-cough dataset and also on a real-time dataset. The evaluation shows that the proposed method has superior performance over the state-of-the-art methods.

关键词： Artificial Intelligence Spatial Pyramid Matching support vector machine classifier

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Tablet identification using support vector machine based text recognition and error correction by enhanced n-grams algorithm

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IET IMAGE PROCESSING 2020年第7期14卷 1366-1372页

作者： Dhivya, A. B. Sundaresan, M. Bharathiar Univ Dept Informat Technol Coimbatore Tamil Nadu India

Unidentified and/or misidentified tablets always present challenges to both patients and health care professionals alike. Consumption of these misidentified tablets often results in adverse drug reaction and sometimes may even cause ill health leading to death. Thus, identification of unknown tablets is an important task in the medical industry. This study proposes an algorithm that uses the text imprinted on the tablet images to identify unknown images. Text imprinted on pills often contains important information that can be used to identify tablets. In this study, multiple feature sets were extracted from tablet images. The proposed work first identifies the text region, from which the text is recognised using support vector machine classifier. A post processing algorithm based on the confusion model combined with n-grams is used to correct the substitution, insertion and deletion errors in the text recognised. Finally, the corrected text is matched with a template tablet database to identify similar tablets. Experimental results showed that the proposed system is efficient with respect to accuracy and can be safely used by both common public and health care professionals to identify tablets.

关键词： support vector machines health care drugs text analysis feature extraction tablet identification support vector machine based text recognition error correction grams algorithm health care professionals misidentified tablets adverse drug reaction ill health unknown tablets tablet images unknown images multiple feature sets text region support vector machine classifier post processing algorithm deletion errors corrected text template tablet database similar tablets common public

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AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration

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IEEE ACCESS 2020年 8卷 149077-149092页

作者： Khan, Baber Ali, Ahmad Jalil, Abdul Mehmood, Khizer Murad, Maria Awan, Hamdan Int Islamic Univ Islamabad Dept Elect Engn Islamabad 44000 Pakistan Bahria Univ Dept Software Engn Islamabad 44000 Pakistan WIT Telecommun Syst & Software Res Grp TSSG Waterford X91 WR86 Ireland

During recent years correlation tracking is considered fast and effective by the virtue of circulant structure of the sampling data for learning phase of filter and Fourier domain calculation of correlation. During the occurrence of occlusion, motion blur and out of view movement of target, most of the correlation filter based trackers start to learn using erroneous samples and tracker starts drifting. Currently, adaptive correlation filter based tracking algorithms are being combined with redetection modules. This hybridization helps in redetection of the target in long term tracking. The redetection modules are mostly classifier, which classify the true object after tracking failure occurrence. The methods perform favorable during short term occlusion or partial occlusion. To further increase the tracking efficiency specifically during long term occlusion, while maintaining real time processing speed, this study proposes tracking failure avoidance method. We first propose, a strategy to detect the occlusion using two cues from the response map i.e., peak correlation score and peak to side lobe ratio. After successful detection of tracking failure, second strategy is proposed to save the target being getting more erroneous. Kalman filter based predictor continuously predicts the location during occlusion. Kalman filter passes this result to support vector machine (SVM). When the target reappears in frame, support vector machine based classifier classifies the correct object using the predicted location of Kalman filter. This decreases the chance of tracking failure as Kalman filter continuously updates itself during occlusion and predicts the next location using its own previous prediction. Once the true object is detected by classifier after the clearance of occlusion, this result is forwarded to correlation filter tracker to resume its operation of tracking and updating its parameters. Together these two proposed schemes show significant improvement in tracking e

关键词： Correlation Target tracking Filtering algorithms Estimation Information filters Adaptive correlation filter Kalman filter occlusion support vector machine classifier

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Smoke detection in ship engine rooms based on video images

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IET IMAGE PROCESSING 2020年第6期14卷 1141-1149页

作者： Park, Kyung-Min Bae, Cherl-O Mokpo Natl Maritime Univ Grad Sch Dept Marine Engn Mokpo 58628 South Korea Mokpo Natl Maritime Univ Dept Marine Engn & Coast Guard Mokpo 58628 South Korea

Fire detection systems in ships are based on smoke and heat detection in accordance with safety regulations. The rapid advancement of machine vision technology has led to the development of video smoke detection (VSD) systems. In this study, a VSD system is applied to smoke detection within the engine room of the ship. A dataset for a range of scenarios was created with a smoke generator. The method for smoke detection was based on motion detection and a support vector machine classifier, which was used to make candidate regions and perform classification. A local binary pattern descriptor was used to extract the feature vector. A training set was made from a variety of video frames, randomly. Experimental results seldom produced false positive windows in the non-smoke region. However, if the greyscale value of difference image between background and the smoke is lower than the setting value for motion detection, the system could not detect smoke. Processing time is sufficiently fast for use in real-time smoke detection systems. To install a VSD system on-board a vessel, the authors recommend a performance standard of the system which must be met.

关键词： support vector machines feature extraction object detection computer vision image classification ships video signal processing smoke smoke detectors fire safety image motion analysis ship engine rooms fire detection systems heat detection video smoke detection systems VSD system smoke generator motion detection support vector machine classifier nonsmoke region real-time smoke detection systems video frames video images local binary pattern descriptor feature vector extraction machine vision

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